Top Machine Learning Jobs in Paris

Conduct product-focused research on machine learning for wearable-device time-series data. Design experiments, build signal-processing and ML pipelines, and develop cognitive and physiological decoding models from multimodal biological signals. Collaborate with hardware, software, and research teams on data capture, model evaluation, and demos, while translating findings into recommendations for leadership. The role requires strong biosignal processing, Python, wearable biometrics, and applied research or engineering experience.

As an AI Researcher at Datadog, you'll conduct cutting-edge research in AI, focusing on foundation models and AI agents for observability and reliability, collaborating with cross-functional teams, and contributing to scientific publications.

The Strategic Sales Executive will sell Identity Governance Administration solutions to multinational organizations, engaging with senior decision-makers and navigating lengthy sales cycles.

Develop and deploy machine learning models and systems for time-series wearable sensing and brain-computer interface applications. Collaborate with research, software, and hardware teams to integrate algorithms, resolve issues, and evaluate model performance. Build training and testing tools, write high-quality code, communicate technical tradeoffs, and apply advanced ML techniques to physiological sensor data and real-time systems.

Applied AI Interns will research and benchmark AI models, develop scalable generative AI and machine learning features, and deploy, monitor, and maintain production AI systems. They will analyze large-scale observability data, investigate emerging model architectures, present research, and collaborate with engineering and product teams to deliver customer-facing capabilities.

Leads a multinational BDR organization of approximately 18 representatives through team leaders. Owns BDR strategy, playbook development, performance management, metrics, sales technology adoption, process optimization, and cross-functional alignment with Sales, Marketing, and senior leadership. The role combines strategic planning with hands-on operational leadership across European and US markets, driving scalable prospecting, qualification, conversion, and team development.

Develop and implement machine learning and deep learning algorithms to analyze MRI data, collaborate with neuroscientists and clinicians to define model requirements, evaluate and optimize models for clinical applications, and write reports and scientific publications.

Own enterprise sales pipelines, manage complex multi-stakeholder SaaS sales cycles, and close strategic deals with Global 2000 organizations. Position Algolia’s AI-powered search, generative AI, and agentic capabilities to executive and technical buyers. Develop territory strategies, expand accounts, generate new opportunities, partner with solutions engineering and strategic partners, create proofs of value and ROI analyses, and forecast accurately using MEDDPICC. Fluency in Dutch and English and 5+ years of enterprise SaaS sales experience are required.

Owns end-to-end data and AI architecture for client engagements, spanning cloud infrastructure, data pipelines, ML model serving, generative AI, and autonomous agents. Leads architecture reviews, technical governance, production readiness, stakeholder engagement, and delivery guidance. Supports pre-sales through technical scoping, proposals, estimates, and client presentations. Mentors engineers, develops reusable IP, and advises on security, compliance, scalability, observability, and cost optimization.

Manage relationships with AI-native startups, scaleups, and labs in France. Understand customer needs, develop strategic opportunities, promote NVIDIA AI platforms, coordinate technical and business resources, build customer references, identify emerging use cases, and represent NVIDIA at industry events. The role requires deep AI industry knowledge, startup ecosystem connections, strategic planning, communication skills, and familiarity with NVIDIA’s software, server, and cloud stack.

Research and develop systematic crypto options trading strategies, from hypothesis and backtesting through production deployment and live management. Responsibilities include analyzing volatility dynamics, building volatility surface models, monitoring P&L, risk, and execution quality, and collaborating with traders and developers on market data and execution infrastructure.

Lead the development and delivery of electronic warfare and signal intelligence systems from concept through field deployment. Responsibilities include RF system architecture, multidisciplinary engineering coordination, feasibility and trade-off analysis, system simulation, integration, prototyping, validation, design reviews, requirements traceability, and process improvement. The role also involves building an EW engineering team and applying RF signal intelligence for classification and AI training.

Advise Mirakl’s legal function on AI, emerging technology, privacy, data protection, regulatory compliance, and intellectual property. Design and deploy AI agents and automation for legal workflows, establish responsible AI policies, lead legal operations transformation, optimize matter management and vendor processes, and train legal, compliance, and business teams on AI-related legal topics.

Lead development and optimization of machine learning, AI, recommendation, and LLM agent solutions for business and marketing use cases. Collaborate with operations, production, IT, analytics, and engineering teams to deploy governed data and agent services. Support MLOps, API deployment, data governance, security compliance, and GenAI adoption while promoting best practices across Ubisoft.

Leads Ipsen’s AI Programming capability within Biometry, establishing strategy, standards, governance, and operating models for AI-enabled programming and automation. Manages clinical data scientists, AI programmers, and application developers while delivering ML, LLM, data products, and applications for clinical and R&D use cases. Partners across Biometry, IT, data and analytics teams, and external vendors to ensure validated, secure, scalable, reproducible solutions and drive organizational adoption.

Develop and productionize NLP and machine learning models for Spanish legal documents, including search, recommendation, information extraction, and conversational agents. Monitor models in production, collaborate with engineering, product, and design teams, contribute to data science best practices and engineering strategy, and stay current with AI research. Strong Python, machine learning, Elasticsearch, and English and Spanish skills are required. The role is based in Paris with hybrid remote flexibility.

Six-month Data and AI internship contributing to machine learning, optimization, generative AI, LLM, and agentic workflow development. Responsibilities include maintaining and retraining models, monitoring production performance, evaluating AI systems, documenting risks and failure modes, supporting responsible AI practices, and explaining data science concepts to deployment teams. The intern will work with Python, data engineering concepts, MLOps, LLMOps, and cloud data platforms in an international Paris-based team.

Leads complex technical programs spanning AI research, robotics hardware, software, simulation, and foundation model training. Builds roadmaps, aligns Bay Area and Paris teams, manages dependencies and risks, establishes execution metrics, and automates program management workflows. Requires technical fluency across machine learning and hardware integration, strong communication, rapid domain learning, decisive execution, and ownership of outcomes.

Designs and deploys AI customer pilots for enterprise and government clients across Southern Europe. Partners with account executives, executives, and technology partners to define use cases, architectures, success metrics, evaluations, and business cases. Serves as the technical advisor throughout customer engagements, gathers product feedback, supports AI adoption, and collaborates with Product and Engineering teams. Requires strong machine learning and LLM knowledge, customer-facing communication, Python skills, and professional English fluency.

Support data processing pipeline deployment, including data reception, cleaning, information extraction, and fraud-model calibration. Conduct R&D in machine learning, NLP, image analysis, trend analysis, and chatbots. Collaborate across sales, legal, product, and engineering throughout the AI product lifecycle.

Build and maintain backend services, APIs, integrations, evaluation systems, data pipelines, and development environments for AI agents. Develop automated evaluations, analyze agent traces and failures, support model integration, and improve service reliability through testing, monitoring, logging, and safe deployments. Collaborate with software engineers, researchers, model-training teams, data specialists, and product owners while writing maintainable Python code and owning scoped components through implementation and operation.

Drives enterprise sales growth for legal and compliance software products through account planning, new customer acquisition, upselling, cross-selling, and strategic relationship management. Leads complex customer engagements, presents tailored solutions, manages sales quotas and forecasts, and collaborates with solution consultants, marketing, professional services, partnerships, and customer success teams. Requires consultative selling, enterprise account management, CRM proficiency, and strong negotiation and presentation skills.

Provides technical leadership for designing, developing, and deploying AI, machine learning, generative AI, and advanced analytics products. Leads solutions from experimentation through production while promoting software engineering, MLOps, testing, governance, responsible AI, and lifecycle best practices. Collaborates with business and product stakeholders, mentors data scientists and machine learning engineers, conducts technical reviews, and ensures scalable, reliable, interpretable, and maintainable AI solutions.

Build and maintain large-scale data pipelines, transform raw location and behavioral data into reliable features, and create datasets for products, analytics, and machine learning. Explore complex data, develop metrics and visualizations, monitor quality, investigate anomalies, and translate exploratory analyses into production systems. Collaborate with product, engineering, and data teams to improve data infrastructure and product intelligence.

Develop machine learning and analytics solutions using large-scale datasets. Responsibilities include feature engineering, predictive modeling, workflow optimization, customer implementations, production deployments, stakeholder collaboration, technical training, and resolving data-related challenges. The role requires Python, PySpark, machine learning pipelines, MLflow or Airflow, statistical modeling, and strong communication skills, with preferred experience in fraud detection, risk analytics, compliance, cloud environments, and model monitoring.